Like many other processes, the programming of media stations has become increasingly reliant on algorithms for selecting and scheduling content. For example, terrestrial radio use qualitative market research and quantitative analytical research to select songs to play and when to play them by taking into account parameters that define the media stations—most importantly genre and demographic information. The results commonly dictate playing a tight rotation of the same 20-30 songs from a pool of as few as 200-300 songs in rotation. Often these songs are selected from a short list of media items being promoted by one or more record labels.
Another common form of algorithmically created media station programming includes Internet radio stations such as PANDORA and IHEART RADIO, among others. PANDORA's media stations are typically characterized by media items that have similar intrinsic musicological attributes to a seed or set of organizing principles governing items played on the media station. PANDORA utilizes a robust database of metadata attributes describing media items and selects media items that have similar metadata attributes.
While popular, PANDORA's stations are prone to playing media that the listener may not like or identify with. This is likely because PANDORA predominately takes into account only one or two types of data at most (i.e. similar types of metadata or musicological attributes) when creating stations.
In addition to algorithmically created media station programming services, there are many websites that publish playlists of media items created by human editors. However, these playlists are made without any regard for the listening tastes of a user.
In an attempt to remedy the deficiencies in the art stemming from only using one type of data, PANDORA now keeps track of media items that a user likes and doesn't like, and plays those media items less frequently, but this isn't a sufficient solution. Utilizing a diverse array of data types has proven difficult.